Best AI papers explained
A podcast by Enoch H. Kang
550 Episoade
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Provably Learning from Language Feedback
Publicat: 09.07.2025 -
Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners
Publicat: 05.07.2025 -
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation
Publicat: 05.07.2025 -
Causal Abstraction with Lossy Representations
Publicat: 04.07.2025 -
The Winner's Curse in Data-Driven Decisions
Publicat: 04.07.2025 -
Embodied AI Agents: Modeling the World
Publicat: 04.07.2025 -
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Publicat: 04.07.2025 -
What Has a Foundation Model Found? Inductive Bias Reveals World Models
Publicat: 04.07.2025 -
Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond
Publicat: 03.07.2025 -
Learning to Explore: An In-Context Learning Approach for Pure Exploration
Publicat: 03.07.2025 -
Human-AI Matching: The Limits of Algorithmic Search
Publicat: 25.06.2025 -
Uncertainty Quantification Needs Reassessment for Large-language Model Agents
Publicat: 25.06.2025 -
Bayesian Meta-Reasoning for Robust LLM Generalization
Publicat: 25.06.2025 -
General Intelligence Requires Reward-based Pretraining
Publicat: 25.06.2025 -
Deep Learning is Not So Mysterious or Different
Publicat: 25.06.2025 -
AI Agents Need Authenticated Delegation
Publicat: 25.06.2025 -
Probabilistic Modelling is Sufficient for Causal Inference
Publicat: 25.06.2025 -
Not All Explanations for Deep Learning Phenomena Are Equally Valuable
Publicat: 25.06.2025 -
e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs
Publicat: 17.06.2025 -
Extrapolation by Association: Length Generalization Transfer in Transformers
Publicat: 17.06.2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
